Learning about objects with human teachers

A general learning task for a robot in a new environment is to learn about objects and what actions/eﬀects they aﬀord. To approach this, we look at ways that a human partner can intuitively help the robot learn, Socially Guided Machine Learning. We present experiments conducted with our robot, Junior, and make six observations characterizing how people approached teaching about objects. We show that Junior successfully used transparency to mitigate errors. Finally, we present the impact of “social” versus “nonsocial” data sets when training SVM classiﬁers.